# Engineering a QoS Provider Mechanism for Edge Computing with Deep Reinforcement Learning

## TL;DR

This paper presents a deep reinforcement learning-based QoS provider mechanism for edge computing, optimizing service delivery in dynamic, distributed environments by identifying and blocking disruptive devices.

## Contribution

It introduces a novel deep Q-learning approach for QoS management in edge computing, addressing the challenges of dynamic resource allocation and system heterogeneity.

## Key findings

- Deep RL outperforms heuristic methods in QoS optimization
- The mechanism effectively identifies disruptive devices in real-time
- Enhanced QoS stability in dynamic edge environments

## Abstract

With the development of new system solutions that integrate traditional cloud computing with the edge/fog computing paradigm, dynamic optimization of service execution has become a challenge due to the edge computing resources being more distributed and dynamic. How to optimize the execution to provide Quality of Service (QoS) in edge computing depends on both the system architecture and the resource allocation algorithms in place. We design and develop a QoS provider mechanism, as an integral component of a fog-to-cloud system, to work in dynamic scenarios by using deep reinforcement learning. We choose reinforcement learning since it is particularly well suited for solving problems in dynamic and adaptive environments where the decision process needs to be frequently updated. We specifically use a Deep Q-learning algorithm that optimizes QoS by identifying and blocking devices that potentially cause service disruption due to dynamicity. We compare the reinforcement learning based solution with state-of-the-art heuristics that use telemetry data, and analyze pros and cons.